Last updated: 2021-01-11

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Knit directory: ~/Research-Local/2020-rnaseq/TCGA-Nigerian-RNAseq/

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#Translation from HTSeq raw counts -> Count Matrix I have 84 TCGA patients with whole-genome sequencing data and RNAseq data as well as 96 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.

                   sampleConditionPAM50
sampleConditionrace Basal Her2 LumA LumB Normal PAM_other
         Nigerian      41   27   14   11      3         0
         TCGA_black    23    0    4    4      0         0
         TCGA_other     0    0    0    0      0        14
         TCGA_white    17    5    8    9      0         0

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis.

##Data visualization

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering. Only ~20% of the distribution of the data set is explained by the PCA1, 2 of the variables.

#Bias in distribution of RNA counts across groups


    Spearman's rank correlation rho

data:  jointcounts$mean.x and jointcounts$mean.y
S = 4.1134e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9678362 

    Spearman's rank correlation rho

data:  jointcounts$sum.x and jointcounts$sum.y
S = 4.1134e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.9678362 

Call:
glm(formula = meancounts ~ condition1 + condition2 + condition1 * 
    condition2, data = outcomecounts_f)

Deviance Residuals: 
      Min         1Q     Median         3Q        Max  
-0.156662  -0.008532   0.006351   0.013649   0.052133  

Coefficients: (3 not defined because of singularities)
                                       Estimate Std. Error  t value Pr(>|t|)
(Intercept)                            6.205609   0.004132 1501.670  < 2e-16
condition1TCGA_black                  -0.047048   0.006893   -6.825 1.90e-10
condition1TCGA_white                  -0.044925   0.007633   -5.886 2.39e-08
condition2Her2                         0.001799   0.006558    0.274    0.784
condition2LumA                         0.005765   0.008191    0.704    0.483
condition2LumB                         0.002947   0.008985    0.328    0.743
condition2Normal                      -0.015785   0.015826   -0.997    0.320
condition1TCGA_black:condition2Her2          NA         NA       NA       NA
condition1TCGA_white:condition2Her2   -0.013656   0.014974   -0.912    0.363
condition1TCGA_black:condition2LumA    0.005072   0.016510    0.307    0.759
condition1TCGA_white:condition2LumA   -0.018886   0.013993   -1.350    0.179
condition1TCGA_black:condition2LumB   -0.012493   0.016918   -0.738    0.461
condition1TCGA_white:condition2LumB   -0.019039   0.014132   -1.347    0.180
condition1TCGA_black:condition2Normal        NA         NA       NA       NA
condition1TCGA_white:condition2Normal        NA         NA       NA       NA
                                         
(Intercept)                           ***
condition1TCGA_black                  ***
condition1TCGA_white                  ***
condition2Her2                           
condition2LumA                           
condition2LumB                           
condition2Normal                         
condition1TCGA_black:condition2Her2      
condition1TCGA_white:condition2Her2      
condition1TCGA_black:condition2LumA      
condition1TCGA_white:condition2LumA      
condition1TCGA_black:condition2LumB      
condition1TCGA_white:condition2LumB      
condition1TCGA_black:condition2Normal    
condition1TCGA_white:condition2Normal    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 0.0007001706)

    Null deviance: 0.21944  on 165  degrees of freedom
Residual deviance: 0.10783  on 154  degrees of freedom
AIC: -721.22

Number of Fisher Scoring iterations: 2

Call:
glm(formula = meancounts ~ condition1 + binarybasal + condition1 * 
    binarybasal, data = outcomecounts_f)

Deviance Residuals: 
      Min         1Q     Median         3Q        Max  
-0.156662  -0.009006   0.005997   0.013868   0.053060  

Coefficients:
                                  Estimate Std. Error  t value Pr(>|t|)    
(Intercept)                       6.207688   0.003534 1756.730  < 2e-16 ***
condition1TCGA_black             -0.048482   0.009916   -4.889 2.45e-06 ***
condition1TCGA_white             -0.061053   0.006611   -9.235  < 2e-16 ***
binarybasal                      -0.002079   0.005407   -0.384    0.701    
condition1TCGA_black:binarybasal  0.001434   0.012039    0.119    0.905    
condition1TCGA_white:binarybasal  0.016128   0.010043    1.606    0.110    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 0.0006867717)

    Null deviance: 0.21944  on 165  degrees of freedom
Residual deviance: 0.10988  on 160  degrees of freedom
AIC: -730.09

Number of Fisher Scoring iterations: 2

Call:
glm(formula = varcounts ~ condition1 + condition2 + condition1 * 
    condition2, data = outcomecounts_f)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.53187  -0.15632  -0.05508   0.10684   1.39968  

Coefficients: (3 not defined because of singularities)
                                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)                           15.191178   0.040119 378.648  < 2e-16 ***
condition1TCGA_black                   0.517159   0.066924   7.728 1.32e-12 ***
condition1TCGA_white                   0.495656   0.074105   6.689 3.92e-10 ***
condition2Her2                        -0.009674   0.063669  -0.152    0.879    
condition2LumA                        -0.054497   0.079519  -0.685    0.494    
condition2LumB                        -0.023179   0.087229  -0.266    0.791    
condition2Normal                       0.187594   0.153646   1.221    0.224    
condition1TCGA_black:condition2Her2          NA         NA      NA       NA    
condition1TCGA_white:condition2Her2    0.134690   0.145376   0.926    0.356    
condition1TCGA_black:condition2LumA   -0.058155   0.160283  -0.363    0.717    
condition1TCGA_white:condition2LumA    0.170866   0.135847   1.258    0.210    
condition1TCGA_black:condition2LumB    0.113024   0.164244   0.688    0.492    
condition1TCGA_white:condition2LumB    0.157791   0.137198   1.150    0.252    
condition1TCGA_black:condition2Normal        NA         NA      NA       NA    
condition1TCGA_white:condition2Normal        NA         NA      NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 0.0659925)

    Null deviance: 22.881  on 165  degrees of freedom
Residual deviance: 10.163  on 154  degrees of freedom
AIC: 33.408

Number of Fisher Scoring iterations: 2

Call:
glm(formula = varcounts ~ condition1 + binarybasal + condition1 * 
    binarybasal, data = outcomecounts_f)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.54130  -0.15692  -0.05639   0.11345   1.39968  

Coefficients:
                                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)                      15.17815    0.03436 441.672  < 2e-16 ***
condition1TCGA_black              0.51878    0.09644   5.379 2.61e-07 ***
condition1TCGA_white              0.63448    0.06429   9.869  < 2e-16 ***
binarybasal                       0.01302    0.05258   0.248    0.805    
condition1TCGA_black:binarybasal -0.00162    0.11708  -0.014    0.989    
condition1TCGA_white:binarybasal -0.13882    0.09767  -1.421    0.157    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 0.0649533)

    Null deviance: 22.881  on 165  degrees of freedom
Residual deviance: 10.393  on 160  degrees of freedom
AIC: 25.118

Number of Fisher Scoring iterations: 2

Call:
glm(formula = sumcounts ~ condition1 + condition2 + condition1 * 
    condition2, data = outcomecounts_f)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3090.0   -168.3    125.3    269.2   1028.3  

Coefficients: (3 not defined because of singularities)
                                       Estimate Std. Error  t value Pr(>|t|)
(Intercept)                           122399.42      81.51 1501.670  < 2e-16
condition1TCGA_black                    -927.98     135.97   -6.825 1.90e-10
condition1TCGA_white                    -886.10     150.55   -5.886 2.39e-08
condition2Her2                            35.48     129.35    0.274    0.784
condition2LumA                           113.70     161.56    0.704    0.483
condition2LumB                            58.13     177.22    0.328    0.743
condition2Normal                        -311.34     312.16   -0.997    0.320
condition1TCGA_black:condition2Her2          NA         NA       NA       NA
condition1TCGA_white:condition2Her2     -269.35     295.35   -0.912    0.363
condition1TCGA_black:condition2LumA      100.03     325.64    0.307    0.759
condition1TCGA_white:condition2LumA     -372.51     275.99   -1.350    0.179
condition1TCGA_black:condition2LumB     -246.40     333.69   -0.738    0.461
condition1TCGA_white:condition2LumB     -375.52     278.74   -1.347    0.180
condition1TCGA_black:condition2Normal        NA         NA       NA       NA
condition1TCGA_white:condition2Normal        NA         NA       NA       NA
                                         
(Intercept)                           ***
condition1TCGA_black                  ***
condition1TCGA_white                  ***
condition2Her2                           
condition2LumA                           
condition2LumB                           
condition2Normal                         
condition1TCGA_black:condition2Her2      
condition1TCGA_white:condition2Her2      
condition1TCGA_black:condition2LumA      
condition1TCGA_white:condition2LumA      
condition1TCGA_black:condition2LumB      
condition1TCGA_white:condition2LumB      
condition1TCGA_black:condition2Normal    
condition1TCGA_white:condition2Normal    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 272391.7)

    Null deviance: 85369567  on 165  degrees of freedom
Residual deviance: 41948320  on 154  degrees of freedom
AIC: 2562.1

Number of Fisher Scoring iterations: 2

Call:
glm(formula = sumcounts ~ condition1 + binarybasal + condition1 * 
    binarybasal, data = outcomecounts_f)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3090.0   -177.6    118.3    273.5   1046.6  

Coefficients:
                                  Estimate Std. Error  t value Pr(>|t|)    
(Intercept)                      122440.43      69.70 1756.730  < 2e-16 ***
condition1TCGA_black               -956.26     195.59   -4.889 2.45e-06 ***
condition1TCGA_white              -1204.21     130.39   -9.235  < 2e-16 ***
binarybasal                         -41.01     106.65   -0.384    0.701    
condition1TCGA_black:binarybasal     28.27     237.46    0.119    0.905    
condition1TCGA_white:binarybasal    318.11     198.08    1.606    0.110    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 267179)

    Null deviance: 85369567  on 165  degrees of freedom
Residual deviance: 42748643  on 160  degrees of freedom
AIC: 2553.3

Number of Fisher Scoring iterations: 2

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

The high rho and significant p-value in the Spearman rank correlation suggests that there is a strong correlation when comparing the means and sums across genes from the Nigerian and TCGA pools. We did not find any subtype specific interaction effects arising within the basal tumors (largest pool)

#Specific gene comparison This section is to compare the distribution/variance in gene counts for specific genes of breast cancer interest across both populations.

#Differential expression setup

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - Basal

[1] 14785    58

       TCGA_white.Basal - Nigerian.Basal
Down                                2344
NotSig                              9126
Up                                  3300

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##VEGF and Claudin gene signatures

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - Basal

[1] 14864    64

       TCGA_black.Basal - Nigerian.Basal
Down                                2571
NotSig                              9017
Up                                  3261

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##EGO/KEGG Pathway analysis: Basal

[1] 14785    58

       TCGA_white.Basal - Nigerian.Basal
Down                                2344
NotSig                              9126
Up                                  3300

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - HER2 (no TCGA Black HER2+ patients)

[1] 13869    32

       TCGA_white.Her2 - Nigerian.Her2
Down                               311
NotSig                           12912
Up                                 631

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##EGO/KEGG Pathway analysis: Her2

[1] 13869    32

       TCGA_white.Her2 - Nigerian.Her2
Down                               311
NotSig                           12912
Up                                 631

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - LumA

[1] 13663    22

       TCGA_white.LumA - Nigerian.LumA
Down                              1086
NotSig                           10938
Up                                1624

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - LumA

[1] 13530    18

       TCGA_black.LumA - Nigerian.LumA
Down                               213
NotSig                           13046
Up                                 256

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##EGO/KEGG Pathway analysis: LumA

[1] 13663    22

       TCGA_white.LumA - Nigerian.LumA
Down                              1086
NotSig                           10938
Up                                1624

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA White - LumB

[1] 13082    20

       TCGA_white.LumB - Nigerian.LumB
Down                               980
NotSig                           10975
Up                                1112

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##DE: Nigerian/TCGA Black - LumB

[1] 12993    15

       TCGA_black.LumB - Nigerian.LumB
Down                                94
NotSig                           12767
Up                                 117

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.

##EGO/KEGG Pathway analysis: LumB

[1] 13082    20

       TCGA_white.LumB - Nigerian.LumB
Down                               980
NotSig                           10975
Up                                1112

Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.


sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] msigdbr_7.2.1               fgsea_1.14.0               
 [3] AnnotationHub_2.20.2        BiocFileCache_1.12.1       
 [5] dbplyr_2.0.0                Glimma_1.16.0              
 [7] preprocessCore_1.50.0       ashr_2.2-47                
 [9] ggfortify_0.4.11            calibrate_1.7.7            
[11] MASS_7.3-53                 sva_3.36.0                 
[13] mgcv_1.8-33                 nlme_3.1-151               
[15] EnsDb.Hsapiens.v75_2.99.0   ensembldb_2.12.1           
[17] AnnotationFilter_1.12.0     GenomicFeatures_1.40.1     
[19] org.Hs.eg.db_3.11.4         hexbin_1.28.2              
[21] stringi_1.5.3               affy_1.66.0                
[23] checkmate_2.0.0             AnnotationDbi_1.50.3       
[25] pathview_1.28.1             clusterProfiler_3.16.1     
[27] pheatmap_1.0.12             genefilter_1.70.0          
[29] vsn_3.56.0                  RUVSeq_1.22.0              
[31] EDASeq_2.22.0               ShortRead_1.46.0           
[33] GenomicAlignments_1.24.0    Rsamtools_2.4.0            
[35] Biostrings_2.56.0           XVector_0.28.0             
[37] BiocParallel_1.22.0         DESeq2_1.28.1              
[39] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
[41] matrixStats_0.57.0          Biobase_2.48.0             
[43] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
[45] IRanges_2.22.2              S4Vectors_0.26.1           
[47] BiocGenerics_0.34.0         edgeR_3.30.3               
[49] limma_3.44.3                RColorBrewer_1.1-2         
[51] ggrepel_0.9.0               forcats_0.5.0              
[53] stringr_1.4.0               dplyr_1.0.2                
[55] purrr_0.3.4                 readr_1.4.0                
[57] tidyr_1.1.2                 tibble_3.0.4               
[59] tidyverse_1.3.0             ggplot2_3.3.3              
[61] gplots_3.1.1               

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.1                rtracklayer_1.48.0           
  [3] R.methodsS3_1.8.1             bit64_4.0.5                  
  [5] knitr_1.30                    irlba_2.3.3                  
  [7] aroma.light_3.18.0            R.utils_2.10.1               
  [9] data.table_1.13.6             hwriter_1.3.2                
 [11] KEGGREST_1.28.0               RCurl_1.98-1.2               
 [13] generics_0.1.0                cowplot_1.1.1                
 [15] RSQLite_2.2.2                 europepmc_0.4                
 [17] bit_4.0.4                     enrichplot_1.8.1             
 [19] xml2_1.3.2                    lubridate_1.7.9.2            
 [21] httpuv_1.5.4                  assertthat_0.2.1             
 [23] viridis_0.5.1                 xfun_0.20                    
 [25] hms_0.5.3                     evaluate_0.14                
 [27] promises_1.1.1                fansi_0.4.1                  
 [29] progress_1.2.2                caTools_1.18.0               
 [31] readxl_1.3.1                  Rgraphviz_2.32.0             
 [33] igraph_1.2.6                  DBI_1.1.0                    
 [35] geneplotter_1.66.0            ellipsis_0.3.1               
 [37] backports_1.2.1               annotate_1.66.0              
 [39] biomaRt_2.44.1                vctrs_0.3.6                  
 [41] withr_2.3.0                   ggforce_0.3.2                
 [43] triebeard_0.3.0               prettyunits_1.1.1            
 [45] DOSE_3.14.0                   lazyeval_0.2.2               
 [47] crayon_1.3.4                  labeling_0.4.2               
 [49] pkgconfig_2.0.3               tweenr_1.0.1                 
 [51] ProtGenerics_1.20.0           rlang_0.4.10                 
 [53] lifecycle_0.2.0               downloader_0.4               
 [55] affyio_1.58.0                 modelr_0.1.8                 
 [57] invgamma_1.1                  cellranger_1.1.0             
 [59] rprojroot_2.0.2               polyclip_1.10-0              
 [61] graph_1.66.0                  Matrix_1.3-2                 
 [63] urltools_1.7.3                reprex_0.3.0                 
 [65] ggridges_0.5.3                png_0.1-7                    
 [67] viridisLite_0.3.0             bitops_1.0-6                 
 [69] R.oo_1.24.0                   KernSmooth_2.23-18           
 [71] blob_1.2.1                    workflowr_1.6.2              
 [73] mixsqp_0.3-43                 SQUAREM_2020.5               
 [75] qvalue_2.20.0                 jpeg_0.1-8.1                 
 [77] gridGraphics_0.5-1            scales_1.1.1                 
 [79] memoise_1.1.0                 magrittr_2.0.1               
 [81] plyr_1.8.6                    zlibbioc_1.34.0              
 [83] compiler_4.0.2                scatterpie_0.1.5             
 [85] KEGGgraph_1.48.0              cli_2.2.0                    
 [87] tidyselect_1.1.0              yaml_2.2.1                   
 [89] GOSemSim_2.14.2               askpass_1.1                  
 [91] locfit_1.5-9.4                latticeExtra_0.6-29          
 [93] grid_4.0.2                    fastmatch_1.1-0              
 [95] tools_4.0.2                   rstudioapi_0.13              
 [97] git2r_0.27.1                  gridExtra_2.3                
 [99] farver_2.0.3                  ggraph_2.0.4                 
[101] digest_0.6.27                 rvcheck_0.1.8                
[103] BiocManager_1.30.10           shiny_1.5.0                  
[105] Rcpp_1.0.5                    broom_0.7.3                  
[107] BiocVersion_3.11.1            later_1.1.0.1                
[109] httr_1.4.2                    colorspace_2.0-0             
[111] rvest_0.3.6                   XML_3.99-0.5                 
[113] fs_1.5.0                      truncnorm_1.0-8              
[115] splines_4.0.2                 graphlayouts_0.7.1           
[117] ggplotify_0.0.5               xtable_1.8-4                 
[119] jsonlite_1.7.2                tidygraph_1.2.0              
[121] R6_2.5.0                      mime_0.9                     
[123] pillar_1.4.7                  htmltools_0.5.0              
[125] fastmap_1.0.1                 glue_1.4.2                   
[127] DESeq_1.39.0                  codetools_0.2-18             
[129] interactiveDisplayBase_1.26.3 lattice_0.20-41              
[131] curl_4.3                      gtools_3.8.2                 
[133] GO.db_3.11.4                  openssl_1.4.3                
[135] survival_3.2-7                rmarkdown_2.6                
[137] munsell_0.5.0                 DO.db_2.9                    
[139] GenomeInfoDbData_1.2.3        haven_2.3.1                  
[141] reshape2_1.4.4                gtable_0.3.0